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# Hoe schaakt een computer? - PowerPoint PPT Presentation

Hoe schaakt een computer?. Arnold Meijster. Why study games?. Why study games? Fun Historically major subject in AI Interesting subject of study because they are hard Games are of interest for AI researchers

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Presentation Transcript
Hoe schaakteen computer?

Arnold Meijster

• Why study games?

• Fun

• Historically major subject in AI

• Interesting subject of study because they are hard

• Games are of interest for AI researchers

• Solution is a strategy (strategy specifies move for every possible opponent reply).

• Time limits force an approximate solution

• Evaluation function: evaluate “goodness” of game position

• Examples: chess, checkers, othello/reversi, backgammon, poker, bridge

• Two players: MAX and MIN

• MAX moves first and they take turns until the game is over.

• Another view: games are a search problem

• Initial state: e.g. board configuration of chess

• Successor function: list of (move,state) pairs specifying legal moves.

• Goal/Terminal test: Is the game finished?

• Utility function: Gives numerical value of terminal states. E.g. win (+1), loose (-1) and draw (0)

• MAX uses a search tree to determine next move.

• Assumption: Both players play optimally !!

• Find the strategy for MAX assuming an infallible MIN opponent.

• Given a game tree, the optimal strategy can be determined by computing the minimax value of each node of the tree:

MINIMAX-VALUE(n)=

UTILITY(n) If n is a terminal

maxs  successors(n) MINIMAX-VALUE(s) If n is a max node

mins  successors(n) MINIMAX-VALUE(s) If n is a min node

Max’s turn

Would like the “9” points (the maximum)

But if Max chooses the left branch, Min will choose the move to get 3

left branch has a value of 3

If Max chooses right, Min can choose any one of 5, 6 or 7 (will choose 5, the minimum)

right branch has a value of 5

Right branch is largest (the maximum) so choose that move

Max

5

3

5

Min

3

9

5

6

7

Max

MinMax – First Example

MinMax – Second Example

MinMax – Pseudo Code

int Max() {

int best = -INFINITY; /* first move is best */

if (isTerminalState()) return Evaluate();

GenerateLegalMoves();

while (MovesLeft()) {

MakeNextMove();

val = Min(); /* Min’s turn next */

UnMakeMove();

if (val > best) best = val;

}

return best;

}

MinMax – Pseudo Code

int Min() {

int best = INFINITY; /*  differs from MAX */

if (isTerminalState()) return Evaluate();

GenerateLegalMoves();

while (MovesLeft()) {

MakeNextMove();

val = Max(); /* Max’s turn next */

UnMakeMove();

if (val < best) //  different than MAX

best = val;

}

return best;

}

• Number of game states explodes when the number of moves increases.

• Solution: Do not examine every node

• Idea: stop evaluating moves when you find a worse result than the previously examined moves.

 Does not benefit the player to play that move, it need not be evaluated any further.

 Save processing time without affecting final result

α is the value of the best (i.e., highest-value) choice found so far at any choice point along the path for max

If v is worse than α, max will avoid it

prune that branch

Define β similarly for min

The α-β pruning algorithm

MinMax – AlphaBeta Pruning Example found so far at any choice point along the path for

• From Max’s point of view, 1 is already lower than 4 or 5, so no need to evaluate 2 and 3 (bottom right)  Prune

Minimax: 2-ply deep found so far at any choice point along the path for

α-β pruning example found so far at any choice point along the path for

α-β pruning example found so far at any choice point along the path for

α-β pruning example found so far at any choice point along the path for

α-β pruning example found so far at any choice point along the path for

α-β pruning example found so far at any choice point along the path for

Properties of α-β found so far at any choice point along the path for

• Pruning does not affect final result (same as minimax)

• Good move ordering improves effectiveness of pruning

• With "perfect ordering," time complexity = O(bm/2)

• Branching factor of sqrt(b) !!

• Alpha-beta pruning can look twice as far as minimax in the same amount of time

• Chess: 4-ply lookahead is a hopeless chess player!

• 4-ply ≈ human novice

• 8-ply ≈ typical PC, human master

• 12-ply ≈ Deep Blue, Kasparov

• Optimization: repeated states are possible.

• Store them in memory = transposition table

MiniMax found so far at any choice point along the path for and Chess

• With a complete tree, we can determine the best possible move

• However, a complete tree is impossible for chess!

• At a given time, chess has ~ 35 legal moves.

• 35 at one ply, 352 = 1225 at two plies … 356 = 2 billion and 3510 = 2 quadrillion

• Games last 40 moves (or more), so 3540

• For large games (like Chess) we can’t see the end of the game.

Games of imperfect information found so far at any choice point along the path for

SHANNON (1950):

• Cut off search earlier (replace TERMINAL-TEST by CUTOFF-TEST)

• Apply heuristic evaluation function EVAL (replacing utility function of alpha-beta)

Heuristic EVAL found so far at any choice point along the path for

• Idea: produce an estimate of the expected utility of the game from a given position.

• Performance depends on quality of EVAL.

• Requirements:

• Computation should not take too long.

• For non-terminal states the EVAL should be strongly correlated with the actual chance of winning.

• Only useful for quiescent (no wild swings in value in near future) states

• Requires quiescence search

Evaluation functions found so far at any choice point along the path for

• Typically a linear weighted sum of features

Eval(s) = w1 f1(s) + w2 f2(s) + … + wnfn(s)

• e.g., w1 = 10 with

f1(s) = (number of white queens) – (number of black queens), etc.

Rule of thumb weight values for chess:

Pawn=1

Bishop, Knight=3

Rook=5

Queen=10

King=99999999

Heuristic difficulties found so far at any choice point along the path for

• Heuristic counts pieces won!

• Consider two cases:

• Black to play

• White to play

• It really makes a difference when to apply the evaluation function!!!

Horizon effect found so far at any choice point along the path for

A program with a fixed depth search

less than 14 will think it can avoid

the queening move

Horizon effect found so far at any choice point along the path for

### Mate in 19! found so far at any choice point along the path for

Mate in 40! found so far at any choice point along the path for

Mate in 40 with Ke7.

Worse are Kc5, Kc6, Kd5, Kd7, Ke5 and Ke6 which throw away the win.

Source: http://www.gilith.com/chess/endgames/kr_kn.html

Mate in 39! found so far at any choice point along the path for

White mates in 39 after Ng7.

Worse are:

Kg4: white mates in 16

Kg5, Kg6, Kh4: white mates in 15

Kh6: white mates in 13

Nc7, Nd6: white mates in 12

Nf6: white mates in 10.

Mate (in 0) found so far at any choice point along the path for

Mate (in 1) found so far at any choice point along the path for

Mate (in 0) found so far at any choice point along the path for

Mate (in 1) found so far at any choice point along the path for

Previous states (black to move) found so far at any choice point along the path for

Nalimov dbase: backward search found so far at any choice point along the path for

for all mate-in-n-positions do

for all reverse moves m by black do

if move m leads (forced) to mate in n

then determine all mate in n+1 positions

Endgame databases… found so far at any choice point along the path for

How end game databases changed chess found so far at any choice point along the path for

• All 5 piece endgames solved (can have > 10^8 states) & many 6 piece

• KRBKNN (~10^11 states): longest path-to-mate 223

• Rule changes

• Max number of moves from capture/pawn move to completion

• Chess knowledge

• KRKN game was thought to be a draw, but

• White wins in 51% of WTM

• White wins in 87% of BTM

Summary found so far at any choice point along the path for

• Games are fun

• They illustrate several important points about AI

• Perfection is unattainable -> approximation

• Uncertainty constrains the assignment of values to states

• A computer’s strength at chess comes from:

• How deep can it search

• How well can it evaluate a board position

• In some sense, like humans – a chess grandmaster can evaluate positions better and can look further ahead

• Games are to AI as grand prix racing is to automobile design.